Mechanical properties of soft tissue correlate with various pathologies. For example: cancerous tissue is often stiffer than non-cancerous tissue; cirrhotic liver tissue is stiffer than normal liver tissue; and an ischemic heart often exhibits abnormal contraction/relaxation of myocardial tissues. The correlation between mechanical properties and pathologies can aid in the diagnosis of pathologies.
Elasticity imaging is a non-invasive method for measuring mechanical properties of soft tissue, which facilitates the diagnosis of various pathologies. Images are taken of soft tissue before and after application of a mechanical force. The images are correlated, and motion or deformation of the tissues can be inferred through a motion tracking algorithm.
An example of a motion tracking algorithm that can be used with elasticity imaging is speckle tracking. When the imaging modality is ultrasound imaging, speckles are formed when transmitted ultrasound waves are reflected by tissues and interfere with each other. Speckle tracking is based on the assumption that speckle patterns remain unchanged before and after tissue motion. The assumption that speckle patterns remain unchanged is used as the basis of motion tracking algorithms; however, speckle patterns do change after tissue deformation. Due to the change in speckle patterns after tissue deformation (“feature-motion decorrelation”), speckle tracking algorithms cannot reveal true tissue motion. Accordingly, speckle tracking algorithms lead to inaccurate estimation of tissue motion and improper inference of mechanical properties.
Motion tracking and geometry are also used in the diagnosis of various pathologies. For example, the mitral valve is a thin leaflet structure that lies between the left atrium and the left ventricle of the heart to control the direction of blood flow. Mitral valve related disease, such as mitral regurgitation, is the most common valvular heart disease. Imaging the heart and acquiring patient specific geometry features of the mitral valve, as well as tracking motion of the mitral valve, can facilitate diagnosis of valvular heart disease and/or assist with surgical intervention for valve repair.
Among various modalities, real time three dimensional echocardiography provides a noninvasive way to model the three dimensional geometry of the mitral valve and to capture its fast motion. To generate a comprehensive mitral valve model, the mitral leaflet is tracked throughout the echocardiographic sequence. To track an object, such as the mitral leaflet, the object is located and segmented throughout an image sequence. The location and segmentation of the mitral leaflet can be done manually, but manual delineation is both labor-intensive and prone to large variance. The variance is especially large for three dimensional images, when only two-dimensional projections or slices of volumetric data can be displayed and processed by the operator at a time. Tracking algorithms are able to track certain objects in natural image analysis. However, tracking the mitral leaflet is extremely difficult and no algorithms exist for such tracking. The mitral valve is especially hard to track due to a lack of reliable features, as well as fast and irregular valve motion.
The above-described background is merely intended to provide an overview of contextual information regarding motion tracking algorithms, and is not intended to be exhaustive. Additional context may become apparent upon review of one or more of the various non-limiting embodiments of the following detailed description.